class_docstr: Data augmentation method in `InstaBoost: Boosting Instance
  Segmentation Via Probability Map Guided Copy-Pasting
  <https://arxiv.org/abs/1908.07801>`_.
  
  Refer to https://github.com/GothicAi/Instaboost for implementation details.
  
  
  Required Keys:
  
  - img (np.uint8)
  - instances
  
  Modified Keys:
  
  - img (np.uint8)
  - instances
  
  Args:
      action_candidate (tuple): Action candidates. "normal", "horizontal", \
          "vertical", "skip" are supported. Defaults to ('normal', \
          'horizontal', 'skip').
      action_prob (tuple): Corresponding action probabilities. Should be \
          the same length as action_candidate. Defaults to (1, 0, 0).
      scale (tuple): (min scale, max scale). Defaults to (0.8, 1.2).
      dx (int): The maximum x-axis shift will be (instance width) / dx.
          Defaults to 15.
      dy (int): The maximum y-axis shift will be (instance height) / dy.
          Defaults to 15.
      theta (tuple): (min rotation degree, max rotation degree). \
          Defaults to (-1, 1).
      color_prob (float): Probability of images for color augmentation.
          Defaults to 0.5.
      hflag (bool): Whether to use heatmap guided. Defaults to False.
      aug_ratio (float): Probability of applying this transformation. \
          Defaults to 0.5.
docstr: Convert raw anns to instaboost expected input format.
docstr_tok: ["Convert","raw","anns","to","instaboost","expected","input","format","."]
code: def _load_anns(self, results: dict) -> Tuple[list, list]:
      """Convert raw anns to instaboost expected input format."""
      anns = []
      ignore_anns = []
      for instance in results["instances"]:
          label = instance["bbox_label"]
          bbox = instance["bbox"]
          mask = instance["mask"]
          x1, y1, x2, y2 = bbox
          # assert (x2 - x1) >= 1 and (y2 - y1) >= 1
          bbox = [x1, y1, x2 - x1, y2 - y1]
  
          if instance["ignore_flag"] == 0:
              anns.append(
                  {
                      "category_id": label,
                      "segmentation": mask,
                      "bbox": bbox,
                  }
              )
          else:
              # Ignore instances without data augmentation
              ignore_anns.append(instance)
      return anns, ignore_anns
  
file_path: mmdetection/mmdet/datasets/transforms/instaboost.py
imports: from typing import Tuple
  import numpy
  from mmcv.transforms import BaseTransform
  from mmdet.registry import TRANSFORMS
label_desc: Use image node_id 2 for calling the InstaBoost obj's underlying member method code with example usage: obj._load_anns(results) and returns: anns,  ignore_anns